Reaching 50 million nanostores : retail distribution in emergingmegacitiesCitation for published version (APA):Blanco, E. E., & Fransoo, J. C. (2013). Reaching 50 million nanostores : retail distribution in emergingmegacities. (BETA publicatie : working papers; Vol. 404). Technische Universiteit Eindhoven.

Document status and date:Published: 01/01/2013

Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)

Please check the document version of this publication:

• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publication

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.

If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverne

Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.

Download date: 23. Jan. 2021

Reaching 50 million nanostores: retail distribution in emerging megacities

Edgar E. Blanco, Jan C. Fransoo

Beta Working Paper series 404

BETA publicatie WP 404 (working paper)

ISBN ISSN NUR

804

Eindhoven January 2013

1

Reaching 50 million nanostores: retail distribution in emerging megacities

Edgar E. Blanco and Jan C. Fransoo

Working Paper, BETA Research School, Eindhoven University of Technology This version: 22 January 2013 Edgar E. Blanco Center for Transportation and Logistics Massachusetts Institute of Technology 77 Massachusetts Ave, Room E40-295 Cambridge, MA 02139 USA eblanco@mit.edu Jan C. Fransoo School of Industrial Engineering Eindhoven University of Technology P.O. Box 513, Pav. F4 5600 MB Eindhoven Netherlands j.c.fransoo@tue.nl A video lecture that incorporates the main messages of this paper can be viewed at http://youtu.be/UsU7hdgsxCs

2

Reaching 50 million nanostores: retail distribution in emerging megacities

Edgar E. Blanco, Center for Transportation and Logistics, Massachusetts Institute of

Technology, USA, eblanco@mit.edu Jan C. Fransoo, School of Industrial Engineering, Eindhoven University of Technology,

Netherlands, j.c.fransoo@tue.nl

Abstract With urbanization sprawling in emerging economies, retail opportunities abound. City structures, income distributions, and shopping patterns in emerging megacities are however very different from the ones we know from developed markets. One of the most dominant characteristics is the presence of millions of very small, family-owned and operated stores. We call these “nanostores”. In this paper, we define and characterize these nanostores, and the associated logistics and channel strategies to reach them as the next opportunity in global retailing. We argue that structures and characteristics are fundamentally different of the developed economies organized big-box retail, and hence new research opportunities can be identified.

1. Introduction In the past decades, research on retail logistics has primarily focused on the operations of organized retail. Retail operations have developed into a well-studied, well-documented field of research and practice, as exemplified for instance by the recent work by Fischer and Raman (2010). With the growth in emerging markets, however, a significant change in the retail landscape in happening. The population in developing economies is rapidly moving into cities, and very large cities are staring to dominate the landscape. In these developing economies, retailing is different. Although the big-box multinational retailers have been present in some of these countries now for several decades, their market share is significant, but very far from the dominance that these retailers experience in developed markets. The traditional retail channel, with its many small stores, are still dominating the retail scene. In contrary to common thinking at the end of the previous century, there is now increasing evidence that the traditional retailer in its small store is there to stay, at least for the coming decades. These small stores, which we will denote as “nanostore” in contrast to the supermarkets or hypermarkets, are the subject of this insight paper. We estimate there are around 50 million nanostores in the developing world, and they are a force to be reckoned with, as their market share in many countries is around half of the total retail market. Not only are nanostores different from an operations perspective (they are not “just” a small supermarket), but also in terms of the distribution to nanostores, and the sales process towards nanostores, these retail channel provide different challenges in supply chain management. These challenges are not only of a practical nature, of relevance to professionals, but also have different theoretical implications, of relevance to academics. In this paper, we will characterize some of the main urbanization trends in developing economies, and their impact on the retail landscape in Section 2. We describe the characteristics of the nanostore in Section 3, and in Section 4 we discuss the various channel strategies. We conclude in Section 5 by highlighting the various opportunities for research.

3

2. Urbanization in developing economies drives retail landscape The speed at which urbanization is happening has increased on the past decade. In particular in developing economies, this is happening at a very fast pace. In 2025, the top 600 cities of the world will cover 22% of the world population, and more than half of the world’s GDP, according to a projection by the McKinsey Global Institute (Dobbs et al., 2011). Most of these large cities will be in what is currently considered the developing economies. According to the same McKinsey projection, of the top 25 megacities in the world, only 4 will be in what is currently considered as the developed world. Moreover, the GDP per capita is increasing substantially in most of these cities, which implies a substantial change is where consumers are. This change is already visible in the growth statistics of Consumer Packaged Goods (CPG) companies. A multinational company like Unilever currently realizes more than half of its revenues in emerging markets, and the growth ambitions for the company towards 2020 need to be realized completely in emerging markets. Within these markets, the cities are the largest share of the economy, both the leading megacities and the second tier of cities. There are multiple definitions of megacities around. Most take the population size as a criterion, with both 5 and 10 million inhabitants used as the cut-off point. Rather than the mere size of the city, from a logistics perspective it is in particular the high population density in combination with its size that makes the megacities in emerging markets very different from the large cities that we know, for instance, from the United States or Europe. In the list of most densely populated large metropolitan areas in the world published by City Majors (2007), the most dense European cities (Athens, London, Madrid) have a density of about 5,000 people per square kilometer. In the US, this is still much lower with most major cities having a density of 2,000 people or less per square kilometer (New York, New Orleans). Of course, Manhattan is much denser at 27,000 people per square kilometer and even just the city of New York counts about 10,000 people per square kilometer. However, compare this to Mumbai (India), where, for the metropolitan area the average population density is about 30,000 people per square kilometer. The consequences for transportation in these cities are serious. Many of these cities are more or less permanently congested. Due to the rapid organic growth of the cities, with little planning, the infrastructure is insufficient to deal with the increase in traffic. Some areas of the city are, because of the width or steepness of the streets, not accessible by trucks or sometimes even by cars. Moreover, these cities are characterized by unprecedented income disparities. As a consequence, the (average) income per capita is not necessarily a good indicator of wealth. In most of these countries, the median income growth is substantially slower than the average. This implies large portions of the population do not have access to transport or any significant amount of disposable cash. Nevertheless, the incomes of hundreds of millions of people have slightly increased, and they are now starting to buy more consumer packaged goods. It is this large group of people, their buying behavior, the stores at which they buy, and the strategies of the CPG manufacturers to reach these new consumers, which is the topic of our research. Unfortunately, large income disparities are often associated with security problems and other problems of crime. Since much of the economy is a cash economy, this puts pressure on the operations, as deliveries of goods and the returning flows of cash are subject to robbery. Furthermore, income disparities also lead to huge geographic differences in the cities. Some cities are very dense, poor neighborhoods, while others may be very affluent. For instance, in the city of Bogota, Colombia, neighborhoods can be found with an average monthly income of

4

just over 100 USD, and a store for every 150 inhabitants, but also adjacent neighborhoods with an average monthly income of 1000 USD, and a store for every 250 inhabitants. This diversity is much beyond the characteristics that can be observed in cities in developed economies, where differences are much smaller.

3. Why is retailing in emerging megacities different: the nanostore In retail, a distinction can be made between the so-called traditional and modern channel. The modern channel is the organized (corporate) retail channel. In this channel, large-scale retailers operate, either with their own stores or franchised stores. These typically large companies have professionals to support the various decision functions, such as marketing, merchandising, category management, logistics, and store operations. In many cases, information systems play a large role, and in much of the supply chains, they are the dominant partner. Consequently, they have a strong position in the price negotiations with the CPG manufacturers. Modern channel retailers do not only exist in developed markets. Many are active in developing markets, both big box international retailers such as Walmart and Carrefour, and convenience store retailers such as 7-11. Also, strong domestic players in emerging markets exist in the organized retail channel, such as Watson in China, Exito in Colombia, and Oxxo in Mexico. The traditional channel is in almost all aspects the opposite of the modern channel. A traditional retailer is usually a family-operated business, with in most cases only one store in the retail company. This implies that all decision functions are united in a single owner-operator. There are different sizes of these businesses: some could be characterized as mini-stores, with 15-40 square meters of store surface (similar to convenience stores in organized retail). Most, however, are much smaller, and have less than 15 square meters of store, or maybe no stores at all but a street cart. We will denote these stores as “nanostores”. A nanostore has a low barrier of entry: in most countries no formal barriers exist in practice to operate a store. The front or garage of a house can easily be converted into a store. Following the 2002 peso crisis in Argentina, the number of stores increased drastically and consumers shifted their shopping to these stores to take advantage of informal credit (McKenzie and Schargrodsky, 2005). Our data suggests that nanostores supply 100-200 consumers in their immediate neighborhood. This implies that they know their customers; they know who has a job and can provide a regular income. Boulaksil (2012) reports that informal credit is granted on an extensive basis, but not to customers that are likely not to pay. The ability to provide informal credit to consumers means that nanostore operators are usually also short of cash. Thus, one of the main challenges in the logistics planning and execution is to time the delivery such that cash is available to pay for the delivery. In most of the developing economies the market share of the traditional channel is substantial. In the 1990s various marketing studies appeared that suggested that with the entry of the big-box retailers, this would soon come to an end. While in some countries, such as Brazil and China, the modern channel has been successful, even in these countries the market share of the modern channel does not exceed 60%. The plateauing of the growth of the modern channel is most likely affected by the limited access of lower income groups to the modern channel. For the hypermarkets, products are typically cheaper, but consumer needs access to transport and to formal credit (or substantial cash) to be able to buy in the large quantities by which products are sold in these stores. In a nanostore, they can very often buy a single cigarette or cookies in a two-pack. The modern convenience channel tries to address this by selling in smaller consumer units. However, the pricing in the modern convenience channel is usually targeted at

5

the middle class consumers, and only competes to a limited extent with the traditional channel; the modern convenience channel does not provide informal credit, requiring consumer to have either cash or access to formal credit. D’Andrea (2006) argues that beyond money and time, also the format of the nanostores is an important contributor to their continued market dominance. Table 1 - Comparing the modern channel supermarket with the traditional channel nanostore Modern Channel

Supermarket Traditional Channel Nanostore

Functions Professionals, dispersed Single store owner-operator Logistics support Distribution centers, cross-

docks, 3PL None

Financial flow Formal credit, bank transfers Cash, relationship-based credit

Line items Full casepacks to store, pallets to retailer DC

Consumer units, mixed casepacks

Number of SKUs Thousands to tens of thousands

Hundreds

Category depth Half dozen to dozens Single or double Number of consumers served per store

Tens of thousands A few hundred

Technology Enterprise systems, POS scanning, EDI

Personal mobile phone

Assortment depth is limited in nanostores. Due to the limited space in the store, owners will typically limit the depth in each category to a very small number: one type of toothpaste or two types of shampoo. Consequently, CPG manufacturers go through large efforts in trying to secure this shelf space. This is often done by claiming space in the store for their entire assortment, for instance by providing a specific display stand. Pre-sellers (see below for more detail) regularly visit the store to ensure replacement of empty spaces on the display stand. The number of nanostores is large. In Mexico, Coca-cola supplies 1.2 million points of sale. In Mexico City alone, Unilever delivers ice cream to more than 10,000 freezers. Bimbo, a Latin American bread producer, directly supplies to 7 million points of sale across Latin America. Based on the five billion consumers that regularly buy in nanostores, and our data on the number of consumers per store, we estimate the total number of nanostores in emerging markets to be at least 50 million. Based on the data presented above on the growth of megacities, it is reasonable to expect that by 2025, at least 10 million of these stores will be present in the world’s top 600 cities. In each of these cities, this implies tens of thousands of points of sale. This leads to different design characteristics in the distribution network. While in North America and Europe, the distribution structure from the manufacturer DC is usually single-tiered (only a single retailer DC or cross-dock), we have observed in Beijing 3 to 4 tiered structures, with multiple handovers of the product to smaller carriers, down to tricycle operators for the last leg. Hence, in nanostores the operation of the stores, the merchandising of the suppliers, and the distribution towards the stores is substantially different from the modern channel, and even substantially different from the modern convenience channel.

6

4. Reaching the nanostores: channel strategies

Frazier and Shervani (1992) defines a route to market as the distinct process through which a product or service can be selected, purchased, ordered, and received by a customer. Anderson et al (1997) argued on the importance of channel design. In traditional manufacturer-retail environments, this decision is often described as a choice between direct store deliveries vs. indirect service models. Supplying nanostores face the same choice. However, due to the unique characteristics of the nanostore there are deeper implications for the manufacturer and a wider range of strategic and operational choices between both models. Figure 1 shows the key functions that need to be considered when serving nanostores: demand generation, order processing, physical distribution, payment collection and after-sale service. When serving the modern retail channel, these functions are usually split among various departments within the retailer and within the manufacturer. For example, the manufacturer sales organization works with the retailer buyer group to determine product launches, promotions and assortment. Accounting and finance departments interface to creating purchase orders and follow agreed payment terms. Warehouse and backroom operators coordinate with delivery vehicles delivery time windows and loading/unloading protocols.

Figure 1 – Key functions that need to be considered when serving nanostores

If a manufacture chooses an indirect service model (via a wholesaler or a distributor) to serve a “modern” retailer, the order processing, physical distribution, payment collection and after-sale service functions are simply considered transactional elements and are evaluated in pure logistics efficiency terms. The demand generation activities are usually maintained within the manufacturer sales and marketing departments, and processes are developed to coordinate with wholesalers and distributors. In the case of nanostores, these functions work in a very different way. First, physical distribution is much more complex. The number of delivery points is larger, with smaller drop sizes and may have higher costs compared to developed market logistics operations. We have found that companies like Colombina (a candy manufacturer) or Coca-Cola, often operate distribution routes with over one hundred stops per day in Bogotá (Colombia). When looking at logistics efficiency metrics, manufacturers often conclude that a wholesaler or a distributor will

7

achieve higher economies of scale and select an indirect service model to reach nanostores. The importance of the other functions is usually ignored. As mentioned earlier, nanostores are often part of the “informal” economy or work with limited assets and no access to credit by financial institutions. As a consequence, cash is usually collected on-delivery. In other words, the physical and monetary flows of the supply chain operate in close proximity and are responsibility of the physical distribution function. This proximity creates an inherent risk to the supply chain that forces companies to design distribution operations that need to handle significant amount of cash. Depending on the risk profile and relationship with its distribution activities, this sometimes means that the vehicle driver is directly responsible of the product (e.g. is in consignment) during the transportation operations and is responsible for its value throughout the distribution process (including theft). In the other extreme, distribution vehicles are equipped with security boxes and tracing systems to safeguard the cash collection process. On the other side, the fast cash-to-cash cycle common when distributing to nanostores, creates opportunities to realize higher profitability due to lower levels of working capital and, if paired with financial analysis, turned into a mechanism to further secure market share by extending credit to nanostores owners. With regard to demand generation, and given the limited shelf space and low technological level of nanostores mentioned earlier, the sales organizations have an interest in having direct contact with nanostore owners. During this contact, products and promotions are evaluated and product orders are placed. This combined demand generation with order processing activity is usually referred to as “pre-sales” and leverages a close connection with the nanostore. Moreover, pre-sales activities often include support on product merchandising to guarantee shelf visibility. Over time, the pre-seller and product delivery staff interacts on a regular basis with the nanostore owner (or his family). This close connection motivates the sales and logistics organization to share responsibilities (e.g. merchandising) and often provide or coordinate any after-sale service processes working as a virtual team. Manufacturers can also opt to completely merge the functions into a single operation referred as “on-board sales”, where the delivery vehicle carries inventory and performs all the activities as part of its order processing is effectively eliminated and demand generation, product delivery, payment and after-sale service are combined in a single interface to the nanostore. Smaller SKU assortment and specially designed vehicles are often used in these operations. Figure 2 summarizes the various options a manufacturer has to reach the nanostore.

8

Figure 2 – Supply choices to nanostores

Leading companies in emerging markets have been using direct service models. Coca-Cola FEMSA, Grupo Bimbo, Grupo Polar, and Alpina are just some example of companies that are using a wide variety of presales, direct store and on-board sales supply schemes to nanostores (Blanco and Garza, 2012). This has enabled higher levels of execution in terms of merchandising and control. This sometimes includes owning or leasing a large share of the vehicle fleet to support distribution operations in urban areas. On the downside, it may results in higher transportation and service costs for the manufacturer. These leading companies, leverage unique business characteristics to streamline distribution operations. In Brazil, using the concept of a “broker” for example, the direct store delivery process may be outsourced for nanostores that are not the very smallest ones. The top 10-20% of stores that have been formally registered and have bank accounts, would allow for a system in which a service provider takes care of the actual delivery while the manufacturer keeps control of the information and manages the credit system. Although this leads to less cash in the system, security concerns still exist on the actual products and adequate risk mitigation strategies need to be negotiated with the “broker”. Multinational manufacturers (MNCs) often rely on indirect service models to reach the nanostores. A major reason for this is that MNCs prefer safety to developing the business opportunity. Direct delivery to nanostores requires knowing the market and its culture, and many MNCs avoid this risk. Further, it requires building up a large system with hundred of people in each city to conduct presales and direct delivery, and potentially also invest in trucking capacity. Using local parties will considerably speed up ramp-up opportunities, but the ceiling of the markets that can be reached through distributors will be reached more quickly. Regional and local manufacturers tend to depend on indirect service models when they face capital and scale constraints. Their choice of direct service to nanostores increases, as they understand the specific requirements of their customers. Table 2 summarizes the strategic trade-offs that companies need to consider as they decide between direct versus indirect strategies to serve nanostores. Developing on-board sales, presales and direct store delivery capabilities allows a closer proximity with the channel, but requires complex operations to balance inventory, SKU assortment and cash flow management. Table 2 – Supply strategies to serve nanostores.

9

Supply Strategy Pros Cons On-board sales • Single point of contact with

nanostore • Full control of all functions and

flows • Urban logistics capabilities

• High logistics cost • Increased inventory • Limited assortment in

vehicle

Presales + Direct Store Delivery

• Control in demand generation • Control of monetary flows • Proximity with nanostore

owners • Urban logistics capabilities

• Logistics complexity • Cash management • Medium/high logistics cost

Presales + Distributor • Active relationship with nanostore

• Reduced distribution and operational costs

• Complex coordination with distributor

• Two points of contact with nanostore

• Loss of urban logistics capabilities

Distributor • Low complexity

• Low cost • Operational influence to

distributor

• Opaque view of nanostores • Reduced influence on

monetary flows • Loss of urban operational

capabilities

Wholesaler • Lowest cost Lowest complexity

• No visibility to nanostores

5. Conclusions and Research Opportunities Nanostores will continue to be a critical retail channel in large urban areas in emerging markets. Designing the right supply strategy needs to balance not only traditional logistics efficiency trade-offs, but increasingly take into account the business impact in demand generation and cash flow management. This has implications both for the private sector, but also for policy makers that are trying to design urban logistics policies.

We believe there are rich research opportunities in logistics and supply chain management tailored to nanostores and their unique urban context:

• Understanding the behavioral and cultural patterns of consumers in urban areas will allow to model consumer choice in the urban context (e.g. mobility) and its impact on the resilience of the nanostore. This includes tracking the growth potential of home-delivery and electronic commerce in low-income urban populations.

• Modeling and optimizing direct vs. indirect channels to service nanostores, including the impact of assortment in nanostore shelf management and buying patterns.

10

• Explicit analysis of cash-to-cash cycles and their impact in designing sales and distribution routes

• Risk management in urban logistics operations (both cash and product) • Vehicle routing in dense and highly congested urban areas • Adoption of mobile technologies in nanostores to enable information sharing and mobile

payments and its impact in urban distribution Finally, it is critical that city logistics research (see Crainic et. al 2004, Taniguchi et. al. 2007, Dablanc, 2007) is updated to recognize the importance of nanostores in megacities in the developing world. The outlined dynamics require policy makers and private actors to explicitly consider the unique relationship between manufacturers and nanostores as they evaluate policy mechanisms. For example, the proximity between the financial and physical flows to nanostores and the fierce competition for shelf space, make less attractive for companies to share logistics providers or participate in urban consolidation centers. Also, adoption of off-hour deliveries initiatives will most likely face similar barriers due to the intense interaction between single-owner nanostores and fragmented distribution operations. There are also unique levers available to urban logistics planners not available in developed markets such as providing access to credit to nanostores or developing risk mitigation incentives to urban logistics operators that may create strong incentives for distribution consolidation and collaboration.

References Anderson, E., Day G.S., and Rangan V.K.. 1997. "Strategic channel design." Sloan Management Review 38 (4): 59-69. Blanco, E.E. and Garza J. 2012. “Distribution Strategies in Emerging Markets: Case Studies in Latin America.” Proceedings of the 4th P&OM World Conference. Boulaksil, Y. 2012. “The operational performance of small retailers in developing countries – the case of Morocco.” Proceedings of the 4th P&OM World Conference. City Majors. 2007. http://www.citymayors.com/statistics/largest-cities-density-125.html, accessed on 7 November 2012. Crainic, T.G., Ricciardi N., and Storchi G. 2004. "Advanced freight transportation systems for congested urban areas." Transportation Research Part C: Emerging Technologies 12 (2): 119-137. Dablanc, L. 2007."Goods transport in large European cities: Difficult to organize, difficult to modernize." Transportation Research Part A: Policy and Practice 41 (3): 280-285. D'Andrea, G., Lopez-Aleman B., and Stengel A. 2006. "Why small retailers endure in Latin America." International Journal of Retail & Distribution Management 34 (9): 661-673. Dobbs, R., Smit, S., Remes, J., Manyika, J., Roxburgh, C., and Restrepo, A. 2011. Urban World: Mapping the economic power of cities. McKinsey Global Institute. Fisher, M., and Raman A. 2010. The new science of retailing: How analytics are transforming the supply chain and improving performance. Harvard Business Press.

11

Frazier, G. L., and Shervani T.1992. "Multiple channels of distribution and their impact on retailing." The future of US retailing: An agenda for the 21st century : 217-237. McKenzie, D., and Schargrodsky E. 2005. "Buying less, but shopping more: Changes in consumption patterns during a crisis." Bureau for Research and Economic Analysis of Development (BREAD) Working Paper 92. Taniguchi, E., Yamada T., and Okamoto M.2007. "Multi-agent modelling for evaluating dynamic vehicle routing and scheduling systems." Journal of the Eastern Asia Society for Transportation Studies 7: 933-948.

Working Papers Beta 2009 - 2013 nr. Year Title Author(s)

404 403 402 401 400 399 398 397 396 395 394

2013 2013 2013 2013 2012 2012 2012 2012 2012 2012 2012

Reaching 50 million nanostores: retail distribution in emerging megacities A Vehicle Routing Problem with Flexible Time Windows The Service Dominant Business Model: A Service Focused Conceptualization Relationship between freight accessibility and Logistics employment in US counties A Condition-Based Maintenance Policy for Multi-Component Systems with a High Maintenance Setup Cost A flexible iterative improvement heuristic to Support creation of feasible shift rosters in Self-rostering Scheduled Service Network Design with Synchronization and Transshipment Constraints For Intermodal Container Transportation Networks Destocking, the bullwhip effect, and the credit Crisis: empirical modeling of supply chain Dynamics Vehicle routing with restricted loading capacities Service differentiation through selective lateral transshipments A Generalized Simulation Model of an Integrated Emergency Post

Edgar E. Blanco, Jan C. Fransoo Duygu Tas, Ola Jabali, Tom van Woensel Egon Lüftenegger, Marco Comuzzi, Paul Grefen, Caren Weisleder Frank P. van den Heuvel, Liliana Rivera, Karel H. van Donselaar, Ad de Jong, Yossi Sheffi, Peter W. de Langen, Jan C. Fransoo Qiushi Zhu, Hao Peng, Geert-Jan van Houtum E. van der Veen, J.L. Hurink, J.M.J. Schutten, S.T. Uijland K. Sharypova, T.G. Crainic, T. van Woensel, J.C. Fransoo Maximiliano Udenio, Jan C. Fransoo, Robert Peels J. Gromicho, J.J. van Hoorn, A.L. Kok J.M.J. Schutten E.M. Alvarez, M.C. van der Heijden, I.M.H. Vliegen, W.H.M. Zijm Martijn Mes, Manon Bruens

393 392 391 390 389 388 387 386 385 384 383 382 381 380

2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012

Business Process Technology and the Cloud: Defining a Business Process Cloud Platform Vehicle Routing with Soft Time Windows and Stochastic Travel Times: A Column Generation And Branch-and-Price Solution Approach Improve OR-Schedule to Reduce Number of Required Beds How does development lead time affect performance over the ramp-up lifecycle? Evidence from the consumer electronics industry The Impact of Product Complexity on Ramp- Up Performance Co-location synergies: specialized versus diverse logistics concentration areas Proximity matters: Synergies through co-location of logistics establishments Spatial concentration and location dynamics in logistics:the case of a Dutch province FNet: An Index for Advanced Business Process Querying Defining Various Pathway Terms The Service Dominant Strategy Canvas: Defining and Visualizing a Service Dominant Strategy through the Traditional Strategic Lens A Stochastic Variable Size Bin Packing Problem With Time Constraints Coordination and Analysis of Barge Container Hinterland Networks

Vasil Stoitsev, Paul Grefen D. Tas, M. Gendreau, N. Dellaert, T. van Woensel, A.G. de Kok J.T. v. Essen, J.M. Bosch, E.W. Hans, M. v. Houdenhoven, J.L. Hurink Andres Pufall, Jan C. Fransoo, Ad de Jong Andreas Pufall, Jan C. Fransoo, Ad de Jong, Ton de Kok Frank P.v.d. Heuvel, Peter W.de Langen, Karel H. v. Donselaar, Jan C. Fransoo Frank P.v.d. Heuvel, Peter W.de Langen, Karel H. v.Donselaar, Jan C. Fransoo Frank P. v.d.Heuvel, Peter W.de Langen, Karel H.v. Donselaar, Jan C. Fransoo Zhiqiang Yan, Remco Dijkman, Paul Grefen W.R. Dalinghaus, P.M.E. Van Gorp Egon Lüftenegger, Paul Grefen, Caren Weisleder Stefano Fazi, Tom van Woensel, Jan C. Fransoo K. Sharypova, T. van Woensel, J.C. Fransoo Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C.

379 378 377 376 375 374 373 372 371 370 369 368 367 366 365

2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2011 2011 2011

Proximity matters: Synergies through co-location of logistics establishments A literature review in process harmonization: a conceptual framework A Generic Material Flow Control Model for Two Different Industries Dynamic demand fulfillment in spare parts networks with multiple customer classes Improving the performance of sorter systems by scheduling inbound containers Strategies for dynamic appointment making by container terminals MyPHRMachines: Lifelong Personal Health Records in the Cloud Service differentiation in spare parts supply through dedicated stocks Spare parts inventory pooling: how to share the benefits Condition based spare parts supply Using Simulation to Assess the Opportunities of Dynamic Waste Collection Aggregate overhaul and supply chain planning for rotables Operating Room Rescheduling Switching Transport Modes to Meet Voluntary Carbon Emission Targets On two-echelon inventory systems with Poisson demand and lost sales

Fransoo Heidi Romero, Remco Dijkman, Paul Grefen, Arjan van Weele S.W.A. Haneya, J.M.J. Schutten, P.C. Schuur, W.H.M. Zijm H.G.H. Tiemessen, M. Fleischmann, G.J. van Houtum, J.A.E.E. van Nunen, E. Pratsini K. Fikse, S.W.A. Haneyah, J.M.J. Schutten Albert Douma, Martijn Mes Pieter van Gorp, Marco Comuzzi E.M. Alvarez, M.C. van der Heijden, W.H.M. Zijm Frank Karsten, Rob Basten X.Lin, R.J.I. Basten, A.A. Kranenburg, G.J. van Houtum Martijn Mes J. Arts, S.D. Flapper, K. Vernooij J.T. van Essen, J.L. Hurink, W. Hartholt, B.J. van den Akker Kristel M.R. Hoen, Tarkan Tan, Jan C. Fransoo, Geert-Jan van Houtum Elisa Alvarez, Matthieu van der Heijden J.T. van Essen, E.W. Hans, J.L. Hurink, A. Oversberg

364 363 362 361 360 359 358 357 356 355 354 353 352 351

2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011

Minimizing the Waiting Time for Emergency Surgery Vehicle Routing Problem with Stochastic Travel Times Including Soft Time Windows and Service Costs A New Approximate Evaluation Method for Two-Echelon Inventory Systems with Emergency Shipments Approximating Multi-Objective Time-Dependent Optimization Problems Branch and Cut and Price for the Time Dependent Vehicle Routing Problem with Time Window Analysis of an Assemble-to-Order System with Different Review Periods Interval Availability Analysis of a Two-Echelon, Multi-Item System Carbon-Optimal and Carbon-Neutral Supply Chains Generic Planning and Control of Automated Material Handling Systems: Practical Requirements Versus Existing Theory Last time buy decisions for products sold under warranty Spatial concentration and location dynamics in logistics: the case of a Dutch provence Identification of Employment Concentration Areas BOMN 2.0 Execution Semantics Formalized as Graph Rewrite Rules: extended version Resource pooling and cost allocation among independent service providers

Duygu Tas, Nico Dellaert, Tom van Woensel, Ton de Kok Erhun Özkan, Geert-Jan van Houtum, Yasemin Serin Said Dabia, El-Ghazali Talbi, Tom Van Woensel, Ton de Kok Said Dabia, Stefan Röpke, Tom Van Woensel, Ton de Kok A.G. Karaarslan, G.P. Kiesmüller, A.G. de Kok Ahmad Al Hanbali, Matthieu van der Heijden Felipe Caro, Charles J. Corbett, Tarkan Tan, Rob Zuidwijk Sameh Haneyah, Henk Zijm, Marco Schutten, Peter Schuur M. van der Heijden, B. Iskandar Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo Pieter van Gorp, Remco Dijkman Frank Karsten, Marco Slikker, Geert-Jan van Houtum E. Lüftenegger, S. Angelov, P. Grefen

350 349 348 347 346 345 344 343 342 341 339 338 335 334 333 332

2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2010 2010 2010 2010 2010 2010

A Framework for Business Innovation Directions The Road to a Business Process Architecture: An Overview of Approaches and their Use Effect of carbon emission regulations on transport mode selection under stochastic demand An improved MIP-based combinatorial approach for a multi-skill workforce scheduling problem An approximate approach for the joint problem of level of repair analysis and spare parts stocking Joint optimization of level of repair analysis and spare parts stocks Inventory control with manufacturing lead time flexibility Analysis of resource pooling games via a new extenstion of the Erlang loss function Vehicle refueling with limited resources Optimal Inventory Policies with Non-stationary Supply Disruptions and Advance Supply Information Redundancy Optimization for Critical Components in High-Availability Capital Goods Analysis of a two-echelon inventory system with two supply modes Analysis of the dial-a-ride problem of Hunsaker and Savelsbergh Attaining stability in multi-skill workforce scheduling Flexible Heuristics Miner (FHM) An exact approach for relating recovering

Remco Dijkman, Irene Vanderfeesten, Hajo A. Reijers K.M.R. Hoen, T. Tan, J.C. Fransoo G.J. van Houtum Murat Firat, Cor Hurkens R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten Ton G. de Kok Frank Karsten, Marco Slikker, Geert-Jan van Houtum Murat Firat, C.A.J. Hurkens, Gerhard J. Woeginger Bilge Atasoy, Refik Güllü, TarkanTan Kurtulus Baris Öner, Alan Scheller-Wolf Geert-Jan van Houtum Joachim Arts, Gudrun Kiesmüller Murat Firat, Gerhard J. Woeginger Murat Firat, Cor Hurkens A.J.M.M. Weijters, J.T.S. Ribeiro P.T. Vanberkel, R.J. Boucherie, E.W. Hans, J.L. Hurink, W.A.M. van Lent, W.H. van Harten

331 330 329 328 327 326 325 324 323 322 321 320 319

2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010

surgical patient workload to the master surgical schedule Efficiency evaluation for pooling resources in health care The Effect of Workload Constraints in Mathematical Programming Models for Production Planning Using pipeline information in a multi-echelon spare parts inventory system Reducing costs of repairable spare parts supply systems via dynamic scheduling Identification of Employment Concentration and Specialization Areas: Theory and Application A combinatorial approach to multi-skill workforce scheduling Stability in multi-skill workforce scheduling Maintenance spare parts planning and control: A framework for control and agenda for future research Near-optimal heuristics to set base stock levels in a two-echelon distribution network Inventory reduction in spare part networks by selective throughput time reduction The selective use of emergency shipments for service-contract differentiation Heuristics for Multi-Item Two-Echelon Spare Parts Inventory Control Problem with Batch Ordering in the Central Warehouse Preventing or escaping the suppression mechanism: intervention conditions

Peter T. Vanberkel, Richard J. Boucherie, Erwin W. Hans, Johann L. Hurink, Nelly Litvak M.M. Jansen, A.G. de Kok, I.J.B.F. Adan Christian Howard, Ingrid Reijnen, Johan Marklund, Tarkan Tan H.G.H. Tiemessen, G.J. van Houtum F.P. van den Heuvel, P.W. de Langen, K.H. van Donselaar, J.C. Fransoo Murat Firat, Cor Hurkens Murat Firat, Cor Hurkens, Alexandre Laugier M.A. Driessen, J.J. Arts, G.J. v. Houtum, W.D. Rustenburg, B. Huisman R.J.I. Basten, G.J. van Houtum M.C. van der Heijden, E.M. Alvarez, J.M.J. Schutten E.M. Alvarez, M.C. van der Heijden, W.H. Zijm B. Walrave, K. v. Oorschot, A.G.L. Romme Nico Dellaert, Jully Jeunet. R. Seguel, R. Eshuis, P. Grefen.

318 317 316 315 314 313

2010 2010 2010 2010 2010 2010 2010

Hospital admission planning to optimize major resources utilization under uncertainty Minimal Protocol Adaptors for Interacting Services Teaching Retail Operations in Business and Engineering Schools Design for Availability: Creating Value for Manufacturers and Customers Transforming Process Models: executable rewrite rules versus a formalized Java program Getting trapped in the suppression of exploration: A simulation model A Dynamic Programming Approach to Multi-Objective Time-Dependent Capacitated Single Vehicle Routing Problems with Time Windows

Tom Van Woensel, Marshall L. Fisher, Jan C. Fransoo. Lydie P.M. Smets, Geert-Jan van Houtum, Fred Langerak. Pieter van Gorp, Rik Eshuis. Bob Walrave, Kim E. van Oorschot, A. Georges L. Romme S. Dabia, T. van Woensel, A.G. de Kok

312 2010 Tales of a So(u)rcerer: Optimal Sourcing Decisions Under Alternative Capacitated Suppliers and General Cost Structures

Osman Alp, Tarkan Tan

311 2010 In-store replenishment procedures for perishable inventory in a retail environment with handling costs and storage constraints

R.A.C.M. Broekmeulen, C.H.M. Bakx

310 2010 The state of the art of innovation-driven business models in the financial services industry

E. Lüftenegger, S. Angelov, E. van der Linden, P. Grefen

309 2010 Design of Complex Architectures Using a Three Dimension Approach: the CrossWork Case R. Seguel, P. Grefen, R. Eshuis

308 2010 Effect of carbon emission regulations on transport mode selection in supply chains

K.M.R. Hoen, T. Tan, J.C. Fransoo, G.J. van Houtum

307 2010 Interaction between intelligent agent strategies for real-time transportation planning

Martijn Mes, Matthieu van der Heijden, Peter Schuur

306 2010 Internal Slackening Scoring Methods Marco Slikker, Peter Borm, René van den Brink

305 2010 Vehicle Routing with Traffic Congestion and Drivers' Driving and Working Rules

A.L. Kok, E.W. Hans, J.M.J. Schutten, W.H.M. Zijm

304 2010 Practical extensions to the level of repair analysis

R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten

303 2010 Ocean Container Transport: An Underestimated and Critical Link in Global Supply Chain Performance

Jan C. Fransoo, Chung-Yee Lee

302 2010 Capacity reservation and utilization for a manufacturer with uncertain capacity and demand

Y. Boulaksil; J.C. Fransoo; T. Tan

300 2009 Spare parts inventory pooling games F.J.P. Karsten; M. Slikker; G.J. van Houtum

299 2009 Capacity flexibility allocation in an outsourced supply chain with reservation Y. Boulaksil, M. Grunow, J.C. Fransoo

298

2010

An optimal approach for the joint problem of level of repair analysis and spare parts stocking

R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten

297 2009 Responding to the Lehman Wave: Sales Forecasting and Supply Management during the Credit Crisis

Robert Peels, Maximiliano Udenio, Jan C. Fransoo, Marcel Wolfs, Tom Hendrikx

296 2009 An exact approach for relating recovering surgical patient workload to the master surgical schedule

Peter T. Vanberkel, Richard J. Boucherie, Erwin W. Hans, Johann L. Hurink, Wineke A.M. van Lent, Wim H. van Harten

295

2009

An iterative method for the simultaneous optimization of repair decisions and spare parts stocks

R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten

294 2009 Fujaba hits the Wall(-e) Pieter van Gorp, Ruben Jubeh, Bernhard Grusie, Anne Keller

293 2009 Implementation of a Healthcare Process in Four Different Workflow Systems

R.S. Mans, W.M.P. van der Aalst, N.C. Russell, P.J.M. Bakker

292 2009 Business Process Model Repositories - Framework and Survey

Zhiqiang Yan, Remco Dijkman, Paul Grefen

291 2009 Efficient Optimization of the Dual-Index Policy Using Markov Chains

Joachim Arts, Marcel van Vuuren, Gudrun Kiesmuller

290 2009 Hierarchical Knowledge-Gradient for Sequential Sampling

Martijn R.K. Mes; Warren B. Powell; Peter I. Frazier

289 2009 Analyzing combined vehicle routing and break scheduling from a distributed decision making perspective

C.M. Meyer; A.L. Kok; H. Kopfer; J.M.J. Schutten

288 2009 Anticipation of lead time performance in Supply Chain Operations Planning

Michiel Jansen; Ton G. de Kok; Jan C. Fransoo

287 2009 Inventory Models with Lateral Transshipments: A Review

Colin Paterson; Gudrun Kiesmuller; Ruud Teunter; Kevin Glazebrook

286 2009 Efficiency evaluation for pooling resources in health care

P.T. Vanberkel; R.J. Boucherie; E.W. Hans; J.L. Hurink; N. Litvak

285 2009 A Survey of Health Care Models that Encompass Multiple Departments

P.T. Vanberkel; R.J. Boucherie; E.W. Hans; J.L. Hurink; N. Litvak

284 2009 Supporting Process Control in Business Collaborations

S. Angelov; K. Vidyasankar; J. Vonk; P. Grefen

283 2009 Inventory Control with Partial Batch Ordering O. Alp; W.T. Huh; T. Tan

282 2009 Translating Safe Petri Nets to Statecharts in a Structure-Preserving Way R. Eshuis

281 2009 The link between product data model and process model J.J.C.L. Vogelaar; H.A. Reijers

280 2009 Inventory planning for spare parts networks with I.C. Reijnen; T. Tan; G.J. van Houtum

delivery time requirements

279 2009 Co-Evolution of Demand and Supply under Competition B. Vermeulen; A.G. de Kok

278 277

2010 2009

Toward Meso-level Product-Market Network Indices for Strategic Product Selection and (Re)Design Guidelines over the Product Life-Cycle An Efficient Method to Construct Minimal Protocol Adaptors

B. Vermeulen, A.G. de Kok R. Seguel, R. Eshuis, P. Grefen

276 2009 Coordinating Supply Chains: a Bilevel Programming Approach Ton G. de Kok, Gabriella Muratore

275 2009 Inventory redistribution for fashion products under demand parameter update G.P. Kiesmuller, S. Minner

274 2009 Comparing Markov chains: Combining aggregation and precedence relations applied to sets of states

A. Busic, I.M.H. Vliegen, A. Scheller-Wolf

273 2009 Separate tools or tool kits: an exploratory study of engineers' preferences

I.M.H. Vliegen, P.A.M. Kleingeld, G.J. van Houtum

272

2009

An Exact Solution Procedure for Multi-Item Two-Echelon Spare Parts Inventory Control Problem with Batch Ordering

Engin Topan, Z. Pelin Bayindir, Tarkan Tan

271 2009 Distributed Decision Making in Combined Vehicle Routing and Break Scheduling

C.M. Meyer, H. Kopfer, A.L. Kok, M. Schutten

270 2009 Dynamic Programming Algorithm for the Vehicle Routing Problem with Time Windows and EC Social Legislation

A.L. Kok, C.M. Meyer, H. Kopfer, J.M.J. Schutten

269 2009 Similarity of Business Process Models: Metics and Evaluation

Remco Dijkman, Marlon Dumas, Boudewijn van Dongen, Reina Kaarik, Jan Mendling

267 2009 Vehicle routing under time-dependent travel times: the impact of congestion avoidance A.L. Kok, E.W. Hans, J.M.J. Schutten

266 2009 Restricted dynamic programming: a flexible framework for solving realistic VRPs

J. Gromicho; J.J. van Hoorn; A.L. Kok; J.M.J. Schutten;

Working Papers published before 2009 see: http://beta.ieis.tue.nl